DOCSLIB.ORG

Efficient Lidar Point Cloud Data Encoding for Scalable Data Management Within the Hadoop Eco-System

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Efficient Lidar Point Cloud Data Encoding for Scalable Data Management Within the Hadoop Eco-System Efficient LiDAR point cloud data encoding for scalable data management within the Hadoop eco-system Vo, A. V., Hewage, C. N. L., Russo, G., Chauhan, N., Laefer, D. F., Bertolotto, M., Le-Khac, N-A., & Ofterdinger, U. (2020). Efficient LiDAR point cloud data encoding for scalable data management within the Hadoop eco- system. In IEEE BigData 2019 Los Angeles, CA, USA (pp. 5644-5653). IEEE . https://doi.org/10.1109/BigData47090.2019.9006044 Published in: IEEE BigData 2019 Los Angeles, CA, USA Document Version: Peer reviewed version Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights Copyright 2019 IEEE. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher. General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [email protected]. Download date:07. Oct. 2021 Efficient LiDAR point cloud data encoding for scalable data management within the Hadoop eco-system Anh Vu Vo Chamin Nalinda Lokugam Hewage Gianmarco Russo School of Computer Science School of Computer Science Department of Computer Science University College Dublin University College Dublin University of Salerno Dublin, Ireland Dublin, Ireland Fisciano, Italy [email protected] [email protected] [email protected] Neel Chauhan Debra F. Laefer Michela Bertolotto Center for Urban Science + Progress Center for Urban Science + Progress School of Computer Science New York University New York University University College Dublin New York, USA New York, USA Dublin, Ireland [email protected] [email protected] [email protected] Nhien-An Le-Khac Ulrich Oftendinger School of Computer Science School of Natural and Built Environment University College Dublin Queen’s University Belfast Dublin, Ireland Belfast, Northern Ireland [email protected] [email protected] Abstract—This paper introduces a novel LiDAR point cloud I. INTRODUCTION data encoding solution that is compact, flexible, and fully sup- ports distributed data storage within the Hadoop distributed computing environment. The proposed data encoding solution is Globally, three-dimensional (3D) data of the Earth’s to- developed based on Sequence File and Google Protocol Buffers. pography is being collected at an unprecedented rate using Sequence File is a generic splittable binary file format built in the Hadoop framework for storage of arbitrary binary data. The aerial Light Detection and Ranging (LiDAR). As an example, key challenge in adopting the Sequence File format for LiDAR the United States is undertaking its first nationwide LiDAR data is in the strategy for effectively encoding the LiDAR data mapping. As part of that, more than 53% of the country has as binary sequences in a way that the data can be represented been mapped and more than 10 trillion collected data points compactly, while allowing necessary mutation. For that purpose, have been made available publicly [1]. Similarly, in Europe, a data encoding solution, based on Google Protocol Buffers (a language-neutral, cross-platform, extensible data serialisation many countries, including Czech, Denmark, England, Finland, framework) was developed and evaluated. Since neither of the the Netherlands, Poland, Slovenia, Spain, and Switzerland, underlying technologies is sufficient to completely and efficiently have completed nation-wide LiDAR mapping with many more represent all necessary point formats for distributed computing, in progress [2]. Extensive, country-scale, LiDAR mapping an innovative fusion of them was required to provide a viable data projects have also been undertaken in Asia in Japan and the storage solution. This paper presents the details of such a data encoding implementation and rigorously evaluates the efficiency Philippines [3] [4]. These projects highlight the need for an of the proposed data encoding solution. Benchmarking was done efficient LiDAR data access system that [1] stores data and against a straightforward, naive text encoding implementation serves retrieval requests (transactional querying), [2] streams using a high-density aerial LiDAR scan of a portion of Dublin, data to other applications (data streaming), and [3] periodically Ireland. The results demonstrated a 6-times reduction in data analyses the accumulated data (batch processing) [5]. volume, a 4-times reduction in database ingestion time, and up to a 5 times reduction in querying time. This paper focuses on presenting a robust and scalable data encoding solution. The data encoding is a component within a complete data storage system that integrates the encoding with other components including data indices, search Index Terms—LiDAR, point cloud, Big Data, Hadoop, HBase, algorithms, and cache strategies. Readers may consult the Google Protocol Buffers, spatial database, data encoding, dis- authors’ previous works for information explicitly on those tributed database, distributed computing other topics [6], [7]). 978-1-7281-0858-2/19/$31.00 © 2019 IEEE II. BACKGROUND Binary encoding is an alternative to text-based encoding. Depending on the expected values and distribution of each LiDAR data are most often available in the format of point attribute, a data type can be selected for each attribute (e.g. clouds, which are collections of discrete, sampling points of 1-bit Boolean value, 1-byte, 2-, 4-, or 8-byte integer, single, or visible surfaces. The essential component of each LiDAR double-floating point number). Binary encoding offers greater data point is its coordinates (i.e. x, y, and z). Apart from flexibility, file size compactness, and better data parsing speed. the point coordinates, each point may have other attributes The main challenge in using binary format is in interoperabil- such as the timestamp and the intensity of the reflected laser ity. Data encoding and decoding require an agreed file format signal. The exact number and type of point attributes depend specification. Additionally, multiple encoders and decoders on the sensing platform and the data processing procedures. might have to be implemented and maintained, if data are For example, aerial LiDAR data often contains the scan angle intended to be transferrable across software written in various rank and edge of flight line, which do not exist in terrestrial programming languages. A variety of binary file formats have LiDAR data. Additionally, depending on how the data are been developed for LiDAR data. In fact, raw data captured by processed, a LiDAR point cloud can be enriched with attributes the LiDAR sensors are typically stored in proprietary binary derived from the post-processing such as classification tags, formats, which can only be interpreted by proprietary software colour integrated from imagery data, and physical simulation provided by the sensors’ manufacturers (e.g. Leica, Riegl). data (e.g. [8], [9]). LiDAR data density varies significantly There are also many vendor-neutral, binary file formats created within a dataset as the density depends on range, incident for LiDAR point cloud data. Most common among them angle, and other project-specific or equipment-specific factors. is the LAS file format developed by the American Society Thus, an assemblage of LiDAR data points collected by of Photogrammetry and Remote Sensing (ASPRS) for aerial multiple platforms (i.e. different aerial and terrestrial sensors) LiDAR data exchange. As of version 1.4 revision 14, the and/or processed by different procedures are highly likely LAS file specification [10] provides 10 different formats for to be heterogeneous in data schema and distribution. Such point data records. Each of the point record formats has a heterogeneity must be accommodated, especially when data fixed structure of attributes and an optional block of extra integration is needed. The variation of data density can be bytes. The extra bytes are provided to make the file format even more extreme when multiple datasets are aggregated from more extensible and to permit storage of user-defined data. disparate sensing platforms (e.g. different aerial and terrestrial Presently, users must choose one amongst the 10 point data sensors). record formats to match their data. If the selected point record As a LiDAR point cloud dataset is essentially a collec- format does not perfectly match the actual data to be stored, tion of point records, each of which contains the point’s the mismatch will leave storage space unoccupied, which coordinates and a set of numeric attributes, a straightforward unnecessarily increases the file size, as well as the file parsing method to encode the data is a textual data encoding method. time. This problem is especially common when the LAS Most LiDAR and point cloud software (e.g. Leica’s Cyclone, format is adopted for storing point cloud derived from non- Riegl’s RiSCAN Pro, Riegl’s RiPROCESS, PointCloudLi- aerial systems. In addition, the LAS format is intended for brary, CloudCompare) support text formats. A typical text- storing LiDAR sensing data only. Furthermore, LAS does not based point cloud data file has each point record stored as support derived data from post-processing (e.g. the shadowing a separate line in the file. The coordinates and attributes are and solar potential calculations shown in [8] and [9]). separated by defined delimiters such as commas and spaces. Other binary formats commonly used for storing LiDAR Each numeric digit is represented as a character via a standard point clouds include the E57 format [11] and the HDF5 coded character set (e.g.
Load more

Recommended publications
  • Parallel Data Analysis Directly on Scientific File Formats
    Parallel Data Analysis Directly on Scientific File Formats Spyros Blanas 1 Kesheng Wu 2 Surendra Byna 2 Bin Dong 2 Arie Shoshani 2 1 The Ohio State University 2 Lawrence Berkeley National Laboratory [email protected] {kwu, sbyna, dbin, ashosani}@lbl.gov ABSTRACT and physics, produce massive amounts of data. The size of Scientific experiments and large-scale simulations produce these datasets typically ranges from hundreds of gigabytes massive amounts of data. Many of these scientific datasets to tens of petabytes. For example, the Intergovernmen- are arrays, and are stored in file formats such as HDF5 tal Panel on Climate Change (IPCC) multi-model CMIP-5 and NetCDF. Although scientific data management systems, archive, which is used for the AR-5 report [22], contains over petabytes such as SciDB, are designed to manipulate arrays, there are 10 of climate model data. Scientific experiments, challenges in integrating these systems into existing analy- such as the LHC experiment routinely store many gigabytes sis workflows. Major barriers include the expensive task of of data per second for future analysis. As the resolution of preparing and loading data before querying, and convert- scientific data is increasing rapidly due to novel measure- ing the final results to a format that is understood by the ment techniques for experimental data and computational existing post-processing and visualization tools. As a con- advances for simulation data, the data volume is expected sequence, integrating a data management system into an to grow even further in the near future. existing scientific data analysis workflow is time-consuming Scientific data are often stored in data formats that sup- and requires extensive user involvement.
    [Show full text]
  • A Metadata Based Approach for Supporting Subsetting Queries Over Parallel HDF5 Datasets
    A Metadata Based Approach For Supporting Subsetting Queries Over Parallel HDF5 Datasets Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Vignesh Santhanagopalan, B.S. Graduate Program in Computer Science and Engineering The Ohio State University 2011 Thesis Committee: Dr. Gagan Agrawal, Advisor Dr. Radu Teodorescu ABSTRACT A key challenge in scientific data management is to manage the data as the data sizes are growing at a very rapid speed. Scientific datasets are typically stored using low-level formats which store the data as binary. This makes specification of the data and processing very hard. Also, as the volume of data is huge, parallel configurations must be used to process the data to enable efficient access. We have developed a data virtualization approach for supporting subsetting queries on scientific datasets stored in native format. The data is stored in Hierarchical Data Format (HDF5) which is one of the popular formats for storing scientific data. Our system supports SQL queries using the Select, From and Where clauses. We support queries based on the dimensions of the dataset and also queries which are based on the dimensions and attributes (which provide extra information about the dataset) of the dataset. In order to support the different types of queries, we have the pre-processing and post-processing modules. We also parallelize the selection queries involving the dimensions and the attributes. Our system offers the following advantages. We provide SQL like abstraction for specifying subsets of interest which is a powerful mechanism.
    [Show full text]
  • HUDDL for Description and Archive of Hydrographic Binary Data
    University of New Hampshire University of New Hampshire Scholars' Repository Center for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping 4-2014 HUDDL for description and archive of hydrographic binary data Giuseppe Masetti University of New Hampshire, Durham, [email protected] Brian R. Calder University of New Hampshire, Durham, [email protected] Follow this and additional works at: https://scholars.unh.edu/ccom Recommended Citation G. Masetti and Calder, Brian R., “Huddl for description and archive of hydrographic binary data”, Canadian Hydrographic Conference 2014. St. John's, NL, Canada, 2014. This Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact [email protected]. Canadian Hydrographic Conference April 14-17, 2014 St. John's N&L HUDDL for description and archive of hydrographic binary data Giuseppe Masetti and Brian Calder Center for Coastal and Ocean Mapping & Joint Hydrographic Center – University of New Hampshire (USA) Abstract Many of the attempts to introduce a universal hydrographic binary data format have failed or have been only partially successful. In essence, this is because such formats either have to simplify the data to such an extent that they only support the lowest common subset of all the formats covered, or they attempt to be a superset of all formats and quickly become cumbersome. Neither choice works well in practice.
    [Show full text]
  • Towards Interactive, Reproducible Analytics at Scale on HPC Systems
    Towards Interactive, Reproducible Analytics at Scale on HPC Systems Shreyas Cholia Lindsey Heagy Matthew Henderson Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Drew Paine Jon Hays Ludovico Bianchi Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Devarshi Ghoshal Fernando Perez´ Lavanya Ramakrishnan Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Abstract—The growth in scientific data volumes has resulted analytics at scale. Our work addresses these gaps. There are in a need to scale up processing and analysis pipelines using High three key components driving our work: Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform • Reproducible Analytics: Reproducibility is a key com- provides core capabilities for interactivity but was not designed ponent of the scientific workflow. We wish to to capture for HPC systems. In this paper, we outline our efforts that the entire software stack that goes into a scientific analy- bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC sis, along with the code for the analysis itself, so that this systems. Our work is grounded in a real world science use case can then be re-run anywhere. In particular it is important - applying geophysical simulations and inversions for imaging to be able reproduce workflows on HPC systems, against the subsurface.
    [Show full text]
  • Parsing Hierarchical Data Format (HDF) Files Karl Nyberg Grebyn Corporation P
    Parsing Hierarchical Data Format (HDF) Files Karl Nyberg Grebyn Corporation P. O. Box 47 Sterling, VA 20167-0047 703-406-4161 [email protected] ABSTRACT This paper presents a description of the creation of a library to parse Hierarchical Data Format (HDF) Files in Ada. It describes a “work in progress” with discussion of current performance, limitations and future plans. Categories and Subject Descriptors D.2.2 [Design Tools and Techniques]: Software Libraries; E.2 [Data Storage Representations]: Object Representation; I.2.10 [Vision and Scene Understanding]: Representations, data structures, and transforms. General Terms Algorithms, Performance, Experimentation, Data Formats. Keywords Ada, HDF. 1. INTRODUCTION Large quantities of scientific data are published each year by NASA. These data are often accompanied by metadata files that describe the contents of individual files of the data. One example of this data is the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) [1]. Each file of data (consisting of satellite imagery) in HDF (Hierarchical Data Format) [2] is accompanied by a metadata file in XML (Extensible Markup Language) [3], encoded according to a published DTD (Document Type Description) that indicates the components and types of data in the metadata file. Each ASTER data file consists of an image of the satellite as it passes over the earth [4]. Information on the location of the data collected as the satellite passes is contained in the metadata file. Over time, multiple images of the same location on earth are obtained. For many purposes of analysis (erosion, building patterns, deforestation, glacier movement, etc.), these images of the same location are compared over time.
    [Show full text]
  • A New Data Model, Programming Interface, and Format Using HDF5
    NetCDF-4: A New Data Model, Programming Interface, and Format Using HDF5 Russ Rew, Ed Hartnett, John Caron UCAR Unidata Program Center Mike Folk, Robert McGrath, Quincey Kozial NCSA and The HDF Group, Inc. Final Project Review, August 9, 2005 THG, Inc. 1 Motivation: Why is this area of work important? While the commercial world has standardized on the relational data model and SQL, no single standard or tool has critical mass in the scientific community. There are many parallel and competing efforts to build these tool suites – at least one per discipline. Data interchange outside each group is problematic. In the next decade, as data interchange among scientific disciplines becomes increasingly important, a common HDF-like format and package for all the sciences will likely emerge. Jim Gray, Distinguished “Scientific Data Management in the Coming Decade,” Jim Gray, David Engineer at T. Liu, Maria A. Nieto-Santisteban, Alexander S. Szalay, Gerd Heber, Microsoft, David DeWitt, Cyberinfrastructure Technology Watch Quarterly, 1998 Turing Award Volume 1, Number 2, February 2005 winner 2 Preservation of scientific data … the ephemeral nature of both data formats and storage media threatens our very ability to maintain scientific, legal, and cultural continuity, not on the scale of centuries, but considering the unrelenting pace of technological change, from one decade to the next. … And that's true not just for the obvious items like images, documents, and audio files, but also for scientific images, … and MacKenzie Smith, simulations. In the scientific research community, Associate Director standards are emerging here and there—HDF for Technology at (Hierarchical Data Format), NetCDF (network the MIT Libraries, Common Data Form), FITS (Flexible Image Project director at Transport System)—but much work remains to be MIT for DSpace, a groundbreaking done to define a common cyberinfrastructure.
    [Show full text]
  • A Very Useful Enhancement for MSC Nastran and Patran
    Tips & Tricks HDF5: A Very Useful Enhancement for MSC Nastran and Patran et’s face it, as FEA engineers we’ve all been As a premier FEA solver that is widely used by the in that situation where the finite element Aerospace and Automotive industries, MSC Nastran L model has been prepared, boundary now takes advantage of the HDF5 file to manage conditions have been checked and the model has and store your FEA data. This can simplify your post been solved, but then it comes time to investigate processing tasks and make data management much the results. It’s not unusual on a large project to easier and simpler (see below). produce different outputs for different purposes: an XDB or OP2 for Patran, a Punch file to pass to So what is HDF5? It is an open source file format, Excel, and the f06 to read the printed output. In data model, and library developed by the HDF Group. addition, your company may have programs that read one of these files to perform special purpose HDF5 has three significant advantages compared to calculations. Managing all these files becomes a previous result file formats: 1) The HDF5 file is project of its own. smaller than XDB and OP2, 2) accessing results is significantly faster with HDF5, and 3) the input and Well let me tell you, there is a better way to do this!! output datablocks are stored in a single, high 82 | Engineering Reality Magazine Before HDF5 After HDF5 precision file. With input and output in the same file, party extensions are available for Python, .Net, and you eliminate confusion and potential errors keeping many other languages.
    [Show full text]
  • Importing and Exporting Data
    Chapter II-9 II-9Importing and Exporting Data Importing Data................................................................................................................................................ 117 Load Waves Submenu ............................................................................................................................ 119 Line Terminators...................................................................................................................................... 120 LoadWave Text Encodings..................................................................................................................... 120 Loading Delimited Text Files ........................................................................................................................ 120 Determining Column Formats............................................................................................................... 120 Date/Time Formats .................................................................................................................................. 121 Custom Date Formats ...................................................................................................................... 122 Column Labels ......................................................................................................................................... 122 Examples of Delimited Text ................................................................................................................... 123 The Load
    [Show full text]
  • Achieving High Performance I/O with HDF5
    Achieving High Performance I/O with HDF5 HDF5 Tutorial @ ECP Annual Meeting 2020 M. Scot Breitenfeld, Elena Pourmal The HDF Group Suren Byna, Quincey Koziol Lawrence Berkeley National Laboratory Houston, TX Feb 6th, 2020 exascaleproject.org Outline, Announcements and Resources INTRODUCTION 2 Tutorial Outline • Foundations of HDF5 • Parallel I/O with HDF5 • ECP HDF5 features and applications • Logistics – Live Google doc for instant questions and comments: https://tinyurl.com/uoxkwaq 3 https://tinyurl.com/uoxkwaq Announcements • HDF5 User Group meeting in June 2020 • The HDF Group Webinars https://www.hdfgroup.org/category/webinar/ – Introduction to HDF5 – HDF5 Advanced Features – HDF5 VOL connectors 4 https://tinyurl.com/uoxkwaq Resources • HDF5 home page: http://hdfgroup.org/HDF5/ • Latest releases: ØHDF5 1.10.6 https://portal.hdfgroup.org/display/support/Downloads ØHDF5 1.12.0 https://gamma.hdfgroup.org/ftp/pub/outgoing/hdf5_1.12/ • HDF5 repo: https://bitbucket.hdfgroup.org/projects/HDFFV/repos/hdf5/ • HDF5 Jira https://jira.hdfgroup.org • Documentation https://portal.hdfgroup.org/display/HDF5/HDF5 5 https://tinyurl.com/uoxkwaq FOUNDATIONS OF HDF5 Elena Pourmal 6 Foundations of HDF5 • Introduction to – HDF5 data model, software and architecture – HDF5 programming model • Overview of general best practices 7 https://tinyurl.com/uoxkwaq Why HDF5? • Have you ever asked yourself: – How do I organize and share my data? – How can I use visualization and other tools with my data? – What will happen to my data if I need to move my application
    [Show full text]
  • ECE 3574: Applied Software Design
    ECE 3574: Applied Software Design Message Serialization Today we are going to see various techniques for serializing objects, converting them to/from byte streams. The ability to exchange objects over files, pipes, sockets, and shared memory is a common task in concurrent programming. I Serialization (Marshalling or Pickling) I Deserialization (Unmarshalling or UnPickling) I QDataStream I Google Protocol buffers In general how do we make an object in memory persistent? We write it to a file. A file is a linear stream of bytes that may be I text-based: human-readable/editable, very portable and future proof, but we have to write parsers, they can be inefficient, can loose precision, take care to handle utf-8 I binary: machine-readable only, have to take care to make portable (Endianess), can easily get lost over time, but can preserve precision To recover the object we read the file back in. Example: ASCII text-based file Suppose we want to store some information about a task in some Todo app. struct Task { std::string description; bool completed; char priority; std::string context; }; Let’s store this as an ASCII text file. See example code: task_text_delimited.cpp There are a few standard text file formats I delimited files: common delimiters ‘,’, ‘:’, ‘|’ I INI files (originally from windows) I XML (heavy, usually overkill) I JSON (similar to xml but lighter) JSON can handle UTF encoding, arrays, many data types, and there are light-weight open-source parsing libraries for it (I like jsoncpp). Example: binary formatted file Binary files use sequences of bytes to store the same representation as in memory.
    [Show full text]
  • HDF5 Portable Parallel - Juelich.De I/O and I/O Data Formats Member of the Helmholtz-Association March March 14Th, 2017 Outline
    Parallel I/O and Portable Data Formats HDF5 Sebastian Lührs [email protected] Association - Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich, March 14th, 2017 Member Member of the Helmholtz Outline . Introduction . Structure of the HDF5 library . Terms and definitions . HDF5 - programming model and API . Creating/opening HDF5 files . Closing HDF5 files and other objects . HDF5 predefined datatypes . Creating dataspaces . Creating datasets . Writing/reading data . Row major / column major Association - . Partial I/O . Parallel HDF5 Member Member of the Helmholtz March 14th, 2017 2 What is HDF5? Hierarchical Data Format . API, data model and file format for I/O management . Tools suite for accessing data in HDF5 format Association - Member Member of the Helmholtz March 14th, 2017 3 HDF5 - Features . Supports parallel I/O . Self describing data model which allows the management of complex data sets . Portable file format . Available on a variety of platforms . Supports C, C++, Fortran 90 and Java . Pythonic interfaces also available . Provides tools to operate on HDF5 files and data Association - Member Member of the Helmholtz March 14th, 2017 4 Layers of the HDF5 Library Application Your Application Object API (C, F90, C++, Java, Python) Library Internals split HDF5 Library stdio MPI I/O Custom files Virtual File I/O Association - Storage device Member Member of the Helmholtz March 14th, 2017 5 File organization . HDF5 file structure corresponds in many respects to a Unix/Linux file system (fs) HDF5 Unix/Linux fs Group ↔ Directory Data set ↔ File Association - Member Member of the Helmholtz March 14th, 2017 6 Terminology File Container for storing data Group Structure which may contain HDF5 objects, e.g.
    [Show full text]
  • HDF5 File Format Reference Manual
    HDF5 Reference Manual Release 1.8.7 May 2011 http://www.HDFGroup.org Copyright Notice and License Terms for HDF5 (Hierarchical Data Format 5) Software Library and Utilities HDF5 (Hierarchical Data Format 5) Software Library and Utilities Copyright 2006-2011 by The HDF Group. NCSA HDF5 (Hierarchical Data Format 5) Software Library and Utilities Copyright 1998-2006 by the Board of Trustees of the University of Illinois. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted for any purpose (including commercial purposes) provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions, and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions, and the following disclaimer in the documentation and/or materials provided with the distribution. 3. In addition, redistributions of modified forms of the source or binary code must carry prominent notices stating that the original code was changed and the date of the change. 4. All publications or advertising materials mentioning features or use of this software are asked, but not required, to acknowledge that it was developed by The HDF Group and by the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign and credit the contributors. 5. Neither the name of The HDF Group, the name of the University, nor the name of any Contributor may be used to endorse or promote products derived from this software without specific prior written permission from The HDF Group, the University, or the Contributor, respectively.
    [Show full text]